diff --git a/model/supervised_finetuning/configs/config.yaml b/model/supervised_finetuning/configs/config.yaml
index 97e37121..616aa828 100644
--- a/model/supervised_finetuning/configs/config.yaml
+++ b/model/supervised_finetuning/configs/config.yaml
@@ -22,7 +22,7 @@ defaults:
   loss_fn: CrossEntropyLoss
   eval_size:
   log_dir: "base"
-  quantization:
+  quantization: false
 
 galactica-125:
   learning_rate: 5e-5
@@ -62,4 +62,4 @@ debug:
   gradient_accumulation_steps: 1
   per_device_train_batch_size: 1
   per_device_eval_batch_size: 1
-  quantization:
+  quantization: false
diff --git a/model/supervised_finetuning/requirements.txt b/model/supervised_finetuning/requirements.txt
index 7d78f36c..c47a1218 100644
--- a/model/supervised_finetuning/requirements.txt
+++ b/model/supervised_finetuning/requirements.txt
@@ -1,4 +1,5 @@
 accelerate==0.15.0
+bitsandbytes==0.36.0.post2
 datasets==2.8.0
 deepspeed==0.7.7
 mpi4py==3.1.4
diff --git a/model/supervised_finetuning/trainer.py b/model/supervised_finetuning/trainer.py
index cb55131d..450854f1 100644
--- a/model/supervised_finetuning/trainer.py
+++ b/model/supervised_finetuning/trainer.py
@@ -3,9 +3,11 @@ import os
 from distutils.util import strtobool
 from typing import Any, Dict, List, Optional, Tuple, Union
 
+import bitsandbytes
 import torch
 from torch import nn
 from transformers import PreTrainedModel, Trainer, TrainingArguments
+from transformers.training_args import OptimizerNames
 from utils import get_dataset, get_loss, get_model, get_tokenizer, read_yamls
 
 os.environ["WANDB_PROJECT"] = "supervised-finetuning"
@@ -130,12 +132,22 @@ if __name__ == "__main__":
 
     train, evals, collate_fn = get_dataset(training_conf, tokenizer)
 
+    optimizer = OptimizerNames.ADAMW_BNB if training_conf.quantization else None
+
+    if training_conf.quantization:
+        for module in model.modules():
+            if isinstance(module, torch.nn.Embedding):
+                bitsandbytes.optim.GlobalOptimManager.get_instance().register_module_override(
+                    module, "weight", {"optim_bits": 32}
+                )
+
     args = TrainingArguments(
         output_dir=f"{training_conf.model_name}-{training_conf.log_dir}-finetuned",
         num_train_epochs=training_conf.num_train_epochs,
         warmup_steps=training_conf.warmup_steps,
         learning_rate=float(training_conf.learning_rate),
         deepspeed="configs/zero_config.json" if training_conf.deepspeed else None,
+        optim=optimizer,
         fp16=True,
         local_rank=training_conf.local_rank,
         gradient_checkpointing=training_conf.gradient_checkpointing,